Key Concepts in CyNET

• Node: A node represents a cell type in the context of immune cell communication networks. Each node corresponds to a specific cell type, and its properties (such as color) can be represented based on various attributes (e.g., expression levels, cell type categories).

• Edge: An edge represents a connection or interaction between two nodes (cell types). The properties of an edge (such as color and width) can be determined based on the strength of the interaction (e.g., correlation values) and the directionality (positive or negative correlation). For example, a positive correlation might be represented by a red edge, while a negative correlation might be represented by a green edge. The width of the edge can indicate the strength of the interaction, with thicker edges representing stronger interactions.

• Network: A network is a collection of nodes and edges that together represent the interactions between different cell types. In the context of immune cell communication, a network can illustrate how different cell types interact with each other based on their abundance and the correlations between them.

• Node Centrality: Node centrality is a measure of the importance or influence of a node within a network. In the context of immune cell communication networks, node centrality can help identify key cell types that play a crucial role in the communication between different cell types. Common centrality measures include degree centrality (number of connections), betweenness centrality (number of times a node acts as a bridge along the shortest path between two other nodes).

• Network Statistics: Network statistics are quantitative measures that describe the overall structure and properties of a network. In the context of immune cell communication networks, network statistics can provide insights into the overall connectivity, density, and modularity of the network. Examples of network statistics include edge density (the ratio of the number of edges to the number of possible edges), graph density (the ratio of the number of edges to the number of nodes), transitivity (the likelihood that two neighbors of a node are also neighbors), assortativity (the tendency of nodes to connect to similar nodes), diameter (the longest shortest path between any two nodes), and average path length (the average number of steps along the shortest paths for all possible pairs of nodes).

• Modularity: Modularity is a measure of the strength of division of a network into modules (also called communities). In the context of immune cell communication networks, modularity can help identify groups of cell types that are more densely connected to each other than to the rest of the network. A high modularity indicates that the network has a strong community structure, which can be important for understanding the functional organization of the immune cell communication network

• Centralization: Centralization is a measure of how much a network is organized around its most central nodes. In the context of immune cell communication networks, centralization can help identify whether the communication is dominated by a few key cell types or if it is more evenly distributed among many cell types. A high centralization score indicates that the network is dominated by a few highly connected nodes, while a low centralization score indicates that the network is more evenly distributed.

• Assortativity: Assortativity is a measure of the tendency of nodes to connect to other nodes that are similar in some way (e.g., degree, attributes). In the context of immune cell communication networks, assortativity can help identify whether similar cell types tend to interact with each other or if there is a preference for interactions between different cell types. A positive assortativity indicates that similar nodes tend to connect to each other, while a negative assortativity indicates that dissimilar nodes tend to connect to each other.

• Graph Density: Graph density is a measure of how many edges are in a network compared to the maximum possible number of edges. In the context of immune cell communication networks, graph density can help assess how interconnected the network is. A high graph density indicates that there are many interactions between cell types, while a low graph density indicates that there are fewer interactions.

• Average Path Length: Average path length is a measure of the average number of steps along the shortest paths for all possible pairs of nodes in a network. In the context of immune cell communication networks, average path length can help assess how efficiently information can be transmitted between different cell types. A shorter average path length indicates that information can be transmitted more quickly between cell types, while a longer average path length indicates that information may take more steps to be transmitted between cell types.

• Diameter: Diameter is a measure of the longest shortest path between any two nodes in a network. In the context of immune cell communication networks, diameter can help assess the maximum distance that information must travel to be transmitted between any two cell types. A smaller diameter indicates that information can be transmitted more quickly between cell types, while a larger diameter indicates that information may take more steps to be transmitted between cell types.

Resources and citations if you use CyNET in your research:

• Manual and Documentation along with example datasets are available on the GitHub repository: https://github.com/pavanish/cynet
• If you use CyNET in your research, please cite the following publications:

Citations:

1. Kumar P, Yeo JG, Poh SL, Hazirah SN, Sutamam NB, Ang WXG, Chellamuthu VR, Wasser M, Thai YK, Leong JY, Ramakrishna L, Arkachaisri T, Albani S. CyNET- a network analysis framework for high dimensional, system level analyses of the functional Immunome. (In press).

2. Kumar P, Lim A, Poh SL, Nur Hazirah S, Chua C, Sutamam N, Arkachaisri T, Yeo JG, Kofidis T, Sorokin V, Lam CSP, Richards AM, Albani S. Pro-inflammatory derangement of the Immuno-Interactome in Heart Failure. Front Immunol. 2022 Mar 15 doi: 10.3389/fimmu.2022.817514. eCollection 2022.

3. Kumar P, Shih DCW, Lim A, Paleja B, Ling S, Li Yun L, Li Poh S, Ngoh A, Arkachaisri T, Yeo JG, Albani S. 2019. Pro-inflammatory, IL-17 pathways dominate the architecture of the immunome in pediatric refractory epilepsy. JCI insight, 2019 March doi: 10.1172/jci.insight.126337.